Can Negative Travel Habits Hinder Positive Travel Behavioural Change under Beijing Vehicle Restrictions?

Given the rapid development of large cities, the residents faced with pressure both at work and in their personal lives tend to solidify their choice of transport modes and form personal travel habits, which in turn leads to higher requirements for urban traffic management. Based on the modified Theory of Planned Behaviour, the structural equation method is employed to explore people’s travel behaviour. It is found that policy attitude, perceived behaviour control, and subjective norms comprehensively affect the residents’ travel intentions under the Vehicle Restrictions in place in Beijing. The residents without private cars display a stronger intention to change their travel choices under the policies. When considering the mediating effect of travel habits between travel intention and travel choice, the impact of the restrictive policies is weakened. Compared with lower-income people, those with higher incomes demonstrate more stable travel habits in response to the effects of the restrictions. The higher the income, the greater the dependence on private cars exhibited by the residents. To summarize, people’s travel habits weaken to some extent the effects of the restrictive policies. Such policies should be created with the explicit aim of gradually changing the people’s habits.</p

Can Negative Travel Habits Hinder Positive Travel Behavioural Change under Beijing Vehicle Restrictions?

Given the rapid development of large cities, the residents faced with pressure both at work and in their personal lives tend to solidify their choice of transport modes and form personal travel habits, which in turn leads to higher requirements for urban traffic management. Based on the modified Theory of Planned Behaviour, the structural equation method is employed to explore people’s travel behaviour. It is found that policy attitude, perceived behaviour control, and subjective norms comprehensively affect the residents’ travel intentions under the Vehicle Restrictions in place in Beijing. The residents without private cars display a stronger intention to change their travel choices under the policies. When considering the mediating effect of travel habits between travel intention and travel choice, the impact of the restrictive policies is weakened. Compared with lower-income people, those with higher incomes demonstrate more stable travel habits in response to the effects of the restrictions. The higher the income, the greater the dependence on private cars exhibited by the residents. To summarize, people’s travel habits weaken to some extent the effects of the restrictive policies. Such policies should be created with the explicit aim of gradually changing the people’s habits.</p

Effects of Different Soccer Stud Configurations on Knee Kinematlcs and Shoe-Surface Traction of Sidestep Cutting Movement on Natural Grass

The purpose of this study was to inwstigate the effect of different stud configuration on knee joint kinematics of sidestep cutting movement on natural grass. A total of 14 amateur soccer players participated in the tests. Participants were asked to complete tasks of 45" sidestep cutting at the speed of 5.0M+-2m/s on natural grass. They selected soccer s h m with firm ground design (FG), artificial ground design (AG) and turf cleats (TF) randomly. During 45" cut, peak knee flexion (p less than 0.001) and abduction angles (

Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community. However, many research endeavors have been focused on developing practical MARL algorithms whose effectiveness has been studied only empirically, thereby lacking theoretical guarantees. As recent studies have revealed, MARL methods often achieve performance that is unstable in terms of reward monotonicity or suboptimal at convergence. To resolve these issues, in this paper, we introduce a novel framework named Heterogeneous-Agent Mirror Learning (HAML) that provides a general template for MARL algorithmic designs. We prove that algorithms derived from the HAML template satisfy the desired properties of the monotonic improvement of the joint reward and the convergence to Nash equilibrium. We verify the practicality of HAML by proving that the current state-of-the-art cooperative MARL algorithms, HATRPO and HAPPO, are in fact HAML instances. Next, as a natural outcome of our theory, we propose HAML extensions of two well-known RL algorithms, HAA2C (for A2C) and HADDPG (for DDPG), and demonstrate their effectiveness against strong baselines on StarCraftII and Multi-Agent MuJoCo tasks

Explaining the differences of gait patterns between high and low-mileage runners with machine learning

Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns

Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples

Research Progress on Non-coding RNAs in Cholesteatoma of the Middle Ear

Cholesteatoma of the middle ear is a common disease in otolaryngology that is receiving increasing attention. It is estimated that over five million people around the world have suffered from middle ear cholesteatoma. The annual incidence of middle ear cholesteatoma has been reported to be 9.2 per 100,000 in adults and 3 per 100,000 in children. Without timely discovery and intervention, cholesteatomas can become perilously large and damage intratemporal structures, causing various intracranial and extracranial complications. No practical nonsurgical treatments are currently available. Although multiple hypotheses exist, research directions have consistently focused on cell proliferation, apoptosis, and bone destruction. Non-coding RNAs (ncRNAs), especially microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have recently received increasing attention because of their key roles in gene expression, cell cycle regulation, and the development of many diseases. Although ncRNAs are not involved in protein translation, they are abundant in the genome, with only approximately 2% of genes encoding proteins and the remaining approximately 98% encoding ncRNAs. The purpose of this review is to summarize the current state of knowledge regarding the specific role of ncRNAs in middle ear cholesteatoma